Using NotebookLM to Support Student Inquiry

NotebookLM, an AI-powered research assistant developed by Google, offers learners a dynamic tool for organizing and synthesizing sources, interacting with texts, and supporting inquiry-based learning. While initially developed for individual research, NotebookLM can be leveraged creatively in classroom settings to enhance student engagement, critical thinking, and digital literacy. Below are three ways to integrate it into your course design, along with guidance on when and how to intervene effectively.


Strategies

Multiperspectives exploration (stations activity)

Goal:
Support multi-source synthesis and critical reading.

Set up multiple digital “stations” or notebooks, each preloaded with a distinct sets of sources such as primary documents, scholarly articles, media excerpts. Students rotate among stations, using NotebookLM to summarize key points, compare arguments, and respond to instructor prompts grounded in the original materials.

Instructor role:

  • Guide the station setup, ensure diverse and balanced sources, and provide a reflection sheet or handout with focused prompts for each rotation.

  • Remind students to verify NotebookLM outputs against the original sources.

Collaborative source validation (jigsaw activity)

Goal:
Strengthen collaborative learning and source evaluation skills.

The jigsaw structure promotes peer teaching, critical thinking, and deeper engagement with course content, while supporting ethical and purposeful use of AI tools. Students begin in “home” groups, then join “expert” groups to explore unique sets of sources using NotebookLM.

Within their expert groups, students use the tool to summarize key ideas, generate questions, and evaluate the credibility of their sources. Students then return to their home groups to teach their peers what they have learned, share insights from NotebookLM, and collaboratively validate claims across the different source sets.

Instructor role:

  • Provide curated sources, design guiding questions, and facilitate group synthesis.

Reflection and revision with Notebook LM

Goal:
Promote metacognitive reflection and research integrity.

After drafting a short essay or research response, students use NotebookLM to cross-check their claims against their sources. This promotes AI-supported self-editing.

Instructor role:

  • Model effective use of NotebookLM, and provide a checklist for revision.


Instructor intervention points

While AI tools like NotebookLM can enhance student autonomy, strategic instructor intervention is essential to ensure effective learning outcomes. Key moments include:

  • Before: Curate or co-select source material, set clear learning outcomes and co-create success criteria with students.

  • During: Monitor student questions in NotebookLM chat logs; facilitate in-the-moment discussion; conduct mini-conferences.

  • After: Provide feedback on synthesis quality, debrief the AI process, and assign metacognitive reflections.


Reflective planning prompts

Use these reflective questions to plan and assess your integration of NotebookLM into your course:

  • How can NotebookLM change the way students engage with course content?

  • What scaffolding or modeling do students need before using Notebook LM independently?

  • At which points in the assignment workflow is instructor intervention most impactful?

  • How can NotebookLM support differentiation or inclusive access for diverse learners?


Learn more: Additional resources

U of A resources

Centre for Teaching and Learning. (2025). Notebook LM: A brief tutorial [Video]. University of Alberta. https://universityofalberta.instructuremedia.com/embed/2d7e273f-5904-4a2a-a8c7-e6f7deeea43e

Information Services & Technology (IST). (2025). Get Started with NotebookLM. University of Alberta. https://www.ualberta.ca/en/information-services-and-technology/news/2025/get-started-with-notebooklm.html

External resources

Acar, O. A. (2025, February 12). Turn your class lessons into engaging podcasts with AI. Harvard Business Impact. https://hbsp.harvard.edu/inspiring-minds/turn-class-lessons-ai-podcasts-accessible-student

Google. (n.d.). NotebookLM help [Support page]. https://support.google.com/notebooklm/?hl=en#topic=16164070

Tufino, E. (2025). NotebookLM as a Socratic physics tutor: Design and preliminary observations of a RAG-based tool. arXiv. https://arxiv.org/abs/2504.09720